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I work with highly seasonal data (city-wide water consumption) and Prophet has been a great tool for us. In terms of performance, it has been the best for a fe
by baladre 5y ago
I work with highly seasonal data (city-wide water consumption) and Prophet has been a great tool for us.
In terms of performance, it has been the best for a few of our forecasts, compared to GRUs, LSTM, ARIMA and SARIMA. When it wasn't the best, it wasn't too far from the best model. But, to be fair, our forecast are of quite stable data, so most models do well.
However, I would say that the key strength of Prophet is how easy it is. You can produce results really fast, you can throw data with missing range, holidays, and it has interpretability components out of the box. It depends on what do you need, but for most of our tasks, we and our stakeholders are more than happy to sacrifice a bit of performance for this features.
- fighterpilot 5y agoHow well does it work with irregularly spaced time series?
- lordgrenville 5y agoJust as well, in my experience, since it's curve-fitting and not autoregressive.
- fighterpilot 5y agoDoes it apply any kind of persistence/memory on the instantaneous exogenous variables you feed it? E.g. if you feed it the exogenous variable of "temperature right now", is it able to create a new exogenous feature "average temperature over the last three time steps"?
- em500 5y agoNo, you have to handcraft all (transformations) of exogenous features. But since it's really all linear regression, that's usually reasonably straightforward.
- deleted 5y ago[deleted]
- microprediction 5y agoDo you have any city data that could be published on an hourly basis? We'd soon see how prophet does against other approaches. Just like this example: https://www.microprediction.org/stream_dashboard.html?stream=hospital-er-wait-minutes-piedmont_henry https://www.microprediction.org/stream_dashboard.html?stream...